• 한국정밀공학회지
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• 제23권11호
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• pp.58-67
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• 2006

Due to the variety of signal processing and complicated mathematical analysis, it is not easy to accomplish 3D bin-picking with non-contact sensor. To solve this difficulties the reliable signal processing algorithm and a good sensing device has been recommended. In this research, 3D laser scanner and CCD camera is applied as a sensing device respectively. With these sensor we develop a two-step bin-picking method and reliable algorithm for the recognition of 3D bin object. In the proposed bin-picking, the problem is reduced to 2D intial recognition with CCD camera at first, and then 3D pose detection with a laser scanner. To get a good movement in the robot base frame, the hand eye calibration between robot's end effector and sensing device should be also carried out. In this paper, we examine auto-calibration technique in the sensor calibration step. A new thinning algorithm and constrained hough transform is also studied for the robustness in the real environment usage. From the experimental results, we could see the robust bin-picking operation under the non-aligned 3D hole object.

• 사물인터넷융복합논문지
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• 제10권5호
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• pp.19-26
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• 2024

본 연구는 제조 분야에서 산업용 로봇을 이용하여 빈 피킹을 구현할 때, 쉽고, 빠르고, 상대적으로 저렴한 비용의 6D 자세 추정 방법을 제시하고 검증하였다. 상세하게는 산업용 로봇과 2D 카메라를 연동하여 ①객체의 다시점 이미지를 획득하고 학습 데이터를 수집하는 방법, ②수집된 데이터에서 변수를 선택하고 선형 회귀 모델을 구현하는 방법, ③학습된 모델을 산업용 로봇에 적용하여 객체의 6D 자세를 추정, 검증 및 평가하는 방법을 제시하였다. 제시된 데이터 수집 방법과 구현된 선형 회귀 모델은 통계적으로 유의한 결과를 보였으며, 추정된 6D 자세는 참값 검증과 산업용 로봇 적용 평가에서 그 타당성을 확인할 수 있었다. 이미지를 직접 입력하는 대신 이미지에서 추출한 특징점 정보를 회귀 모델의 입력으로 사용함으로써 데이터의 크기를 줄이고 로봇에 직접 임베딩(embedding) 할 수 있었다. 본 연구는 3D 공간의 좌표 문제를 기하학이나 컴퓨터 비전의 관점이 아닌 데이터 분석의 관점에서 접근하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.231-233
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• 2007

In this paper we present a new visual approach for the robust bin-picking in a two-step concept for a vision driven automatic handling robot. The technology described here is based on two types of sensors: 3D laser scanner and CCD video camera. The geometry and pose(position and orientation) information of bin contents was reconstructed from the camera and laser sensor. these information can be employed to guide the robotic arm. A new thinning algorithm and constrained hough transform method is also explained in this paper. Consequently, the developed bin-picking demonstrate the successful operation with 3D hole object.

• 한국산업융합학회 논문집
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• 제26권2_2호
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• pp.249-254
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• 2023

The use of robots in automated factories requires accurate bin-picking to ensure that objects are correctly identified and selected. In the case of atypical objects with multiple reflections from their surfaces, this is a challenging task. In this paper, we developed a random 3D bin picking system by integrating the low-cost vision system with the robotics system. The vision system identifies the position and posture of candidate parts, then the robot system validates if one of the candidate parts is pickable; if a part is identified as pickable, then the robot will pick up this part and place it accurately in the right location.

• 한국생산제조학회지
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• 제25권6호
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• pp.522-531
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• 2016

In this study, we propose an object identification method for bin-picking developed for industrial robots. We identify the grasp posture and the associated geometric parameters of grasp objects using the joint data of a robotic gripper. Prior to grasp identification, we analyze the grasping motion in a low-dimensional space using principle component analysis (PCA) to reduce the dimensions. We collected the joint data from a human hand to demonstrate the grasp-identification algorithm. For data acquisition of the human grasp data, we conducted additional research on the motion characteristics of a human hand. We explain the method for using the algorithm of grasp identification for bin-picking. Finally, we present a subject for future research using our proposed algorithm of grasp model and identification.